SOTAVerified

Semantic Similarity

The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.

Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–950 of 1564 papers

TitleStatusHype
TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA—0
Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection—0
Targeted and Troublesome: Tracking and Advertising on Children's Websites—0
Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering—0
Task-Oriented Intrinsic Evaluation of Semantic Textual Similarity—0
Team dina at SemEval-2022 Task 8: Pre-trained Language Models as Baselines for Semantic Similarity—0
Team GPLSI. Approach for automated fact checking—0
Temporal Analysis of Entity Relatedness and its Evolution using Wikipedia and DBpedia—0
Term Based Semantic Clusters for Very Short Text Classification—0
Testing the limits of unsupervised learning for semantic similarity—0
TexIm FAST: Text-to-Image Representation for Semantic Similarity Evaluation using Transformers—0
Text Classification with Negative Supervision—0
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach—0
Text Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual Similarity—0
TextGraphs-16 Natural Language Premise Selection Task: Zero-Shot Premise Selection with Prompting Generative Language Models—0
Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation—0
Texts in, meaning out: neural language models in semantic similarity task for Russian—0
The Case for Perspective in Multimodal Datasets—0
The Challenge of Composition in Distributional and Formal Semantics—0
The Corpus Replication Task—0
The Effect of Negative Sampling Strategy on Capturing Semantic Similarity in Document Embeddings—0
The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity—0
The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain—0
The semantic similarity ensemble—0
The strange geometry of skip-gram with negative sampling—0
The Struggle with Academic Plagiarism: Approaches based on Semantic Similarity—0
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA—0
The word entropy of natural languages—0
THU\_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model—0
Time-Aware Evidence Ranking for Fact-Checking—0
'Tis but Thy Name: Semantic Question Answering Evaluation with 11M Names for 1M Entities—0
Token-Level Privacy in Large Language Models—0
TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks—0
Toward a Computational Multidimensional Lexical Similarity Measure for Modeling Word Association Tasks in Psycholinguistics—0
Toward a Realistic Benchmark for Out-of-Distribution Detection—0
Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation—0
Towards a Gold Standard for Evaluating Danish Word Embeddings—0
Towards Automated Patent Workflows: AI-Orchestrated Multi-Agent Framework for Intellectual Property Management and Analysis—0
Towards Automatic Thesaurus Construction and Enrichment.—0
Towards explainable evaluation of language models on the semantic similarity of visual concepts—0
Towards Label-Only Membership Inference Attack against Pre-trained Large Language Models—0
Towards Semantic Communications: Deep Learning-Based Image Semantic Coding—0
Towards Universal Dense Retrieval for Open-domain Question Answering—0
Traffic event description based on Twitter data using Unsupervised Learning Methods for Indian road conditions—0
Training a Ranking Function for Open-Domain Question Answering—0
Transfer Reward Learning for Policy Gradient-Based Text Generation—0
Transformer-based Joint Source Channel Coding for Textual Semantic Communication—0
Transformers Can Compose Skills To Solve Novel Problems Without Finetuning—0
Transformer Semantic Genetic Programming for Symbolic Regression—0
Translating away Translationese without Parallel Data—0
Show:102550
← PrevPage 19 of 32Next →
All datasetsAnnotated corpus for semantic similarity of clinical trial outcomes (expanded corpus)Annotated corpus for semantic similarity of clinical trial outcomes (original corpus)SICKBIOSSESCHIP-STSClinicalSTSMedSTS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F193.38—Unverified
2SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F191.51—Unverified
3SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F190.69—Unverified
4BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F189.16—Unverified
5BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F189.12—Unverified
#ModelMetricClaimedVerifiedStatus
1BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.75—Unverified
2SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.3—Unverified
3SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.3—Unverified
4BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F186.8—Unverified
5BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F184.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Doc2VecCMSE0.31—Unverified
2LSTM (Tai et al., 2015)MSE0.28—Unverified
3Bidirectional LSTM (Tai et al., 2015)MSE0.27—Unverified
4combine-skip (Kiros et al., 2015)MSE0.27—Unverified
5Dependency Tree-LSTM (Tai et al., 2015)MSE0.25—Unverified
#ModelMetricClaimedVerifiedStatus
1BioLinkBERT (large)Pearson Correlation0.94—Unverified
2BioLinkBERT (base)Pearson Correlation0.93—Unverified
3NCBI_BERT(base) (P+M)Pearson Correlation0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1MacBERT-largeMacro F185.6—Unverified
#ModelMetricClaimedVerifiedStatus
1CharacterBERT (base, medical, ensemble)Pearson Correlation85.62—Unverified
#ModelMetricClaimedVerifiedStatus
1NCBI_BERT(base) (P+M)Pearson Correlation0.85—Unverified